Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/235589
Autoren: 
Li, Wei
Paraschiv, Florentina
Sermpinis, Georgios
Datum: 
2021
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2021-010
Zusammenfassung: 
The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.
Schlagwörter: 
Case-based reasoning
Financial risk detection
Multiple-criteria decision-making
Feature scoring
Particle swarm optimization
Parallel computing
JEL: 
C51
C52
C53
C61
C63
D81
G21
G32
Dokumentart: 
Working Paper

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